Using in vitro methods to investigate individual and combined effects of prebiotics, polyphenols, and whey protein on the human gut microbiome

This abstract was awarded the student prize for best poster presentation. Diet represents one of the most influential modifiable factors shaping gut microbial composition and metabolism. Prebiotics, polyphenols, and proteins each affect the microbiome through distinct mechanisms. Prebiotics, such as the dietary fiber Inulin, promote growth of saccharolytic bacteria and SCFA production (1) . Bioactive plant polyphenols, such as grapeseed extract, can modulate microbial metabolism, exert antimicrobial properties against select pathogens, and generate bioactive metabolites involved in host inflammatory processes (1) . Whey protein serves as a substrate for proteolytic bacteria and microbial amino acid bio-transformations, affecting microbial composition and metabolite profiles (2) . While these components have established individual effects, their combined influence remains underexplored. This study examined the individual and combined effects of pre-digested inulin, grapeseed extract, and whey protein isolate on gut microbial composition and metabolism using the MiGut in vitro colon model. The novel MiGut platform is a miniaturised triple-stage continuous flow in vitro gut model (3) that simulates proximal to distal human colon environments. We used MiGut to undertake a 6-week experiment using eight MiGut reactors (one per treatment condition for each of two healthy human donors). Donor faecal inocula were introduced, followed by a 2-week equilibration period establishing steady-state conditions. Experimental treatments (inulin, grapeseed extract, whey protein isolate, or a combination of all three) were added daily at doses equivalent to dietary recommendations for 2 weeks. All treatments underwent pre-digestion using the INFOGEST in vitro digestion protocol prior to addition (4) . This was followed by a 2-week recovery phase without supplementation to assess microbiome elasticity. Samples were collected at each phase from all colon regions . Statistical analysis used a linear mixed-effects model, with Treatment, State, and Vessel as fixed effects, and Donor as a random effect. Significance was assessed using Type III ANOVA with p < 0.05. PCR analysis revealed distinctive treatment effects relative to baseline. All fold change values reflect differences in microbial abundance compared to post-equilibration levels. Significant treatment effects were observed for Akkermansia, Bacteroides, Bifidobacterium, and Enterococcus (all p<0.05). Inulin increased Bifidobacterium 1.9-fold while reducing Enterobacteriaceae. Whey protein increased Lactobacillus 3.8-fold; grapeseed extract increased Bacteroides by 9.3-fold (p<0.05). The combination treatment demonstrated distinct effects from individual components, enhancing Bifidobacterium populations (2.4-fold increase, p<0.05), but unexpectedly reduced beneficial butyrate-producing bacteria like Roseburia , unlike the individual treatments. Temporal and spatial variations were detected for multiple bacterial targets. Upcoming 16S rRNA sequencing and SCFA analysis will provide deeper taxonomic and functional context. Findings confirm the importance of assessing individual dietary components and their interactions in microbiome research. The MiGut platform was effective in modelling microbial responses to pre-digested nutrients. Microbiome profiling and metabolomics will provide deeper insights into functional outcomes. This work advances understanding of how multi-nutrient approaches may optimise microbiome-targeted interventions for gut health.